AM-GESG identification algorithms for general stochastic systems

Difficulty of parameter identification for general stochastic systems is there exist both unknown noise-free outputs (i.e., true outputs) and unmeasurable noise terms in the information vector. Using the auxiliary model identification technique to establish an auxiliary model based on the measurable input-output data of the system and replacing the unknown noise-free outputs in the information vector with the outputs of the auxiliary model and noise terms in the information vector with the estimated noise values, we present an auxiliary model based generalized extended stochastic gradient (AMGESG) identification algorithm. The algorithm proposed has significant computational advantage over existing least squares identification algorithms. The simulation example indicates that the parameter estimation errors become small as the data length increases.